1. Field of the Invention
This invention relates to an element that serves as the terminal or end point for a control system. Such element uses a controllable plant to interface with the environment and accomplish the job function. The plant is controlled by an actuating signal which is generated within the terminal element by three distinct algorithmic processes: a real time plant output simulator, a true-to-life output adjustor, and a control signal processor. The output simulator and the output adjustor yield a comparative plant output signal that is compared with the desired plant output signal to obtain an error signal which the control signal processor uses to establish the actuating signal for the controllable plant.
The comparative plant output signal approach of the invention is unique because it utilizes available, readily measurable physical operating parameters of the actual controllable plant and the operating environment by using real time plant simulation and output adjustment algorithms to accurately represent the plant's actual output which may be a signal that is often impossible to obtain physically, practically, or economically. A terminal element does not need or use the actual plant output for control or for other purposes. The output from the real time plant simulator is modified by a hierarchical adjustment algorithm to accurately represent the actual plant output (referred to herein as the comparative plant output signal) and to account for changes in the normal operating parameters (NOP), the environmental forcing parameters (EFP), the symptomatic or diagnostics monitoring parameters (SMP), and the calibration adjustment parameters (CAP).
The controller for a terminal element uses the tracking error (the real time difference between the desired plant output signal and the comparative plant output signal) to generate a plant actuating or control signal for regulating the output of the plant. The controller can use simple feedback control, optimal control, adaptive control, or learning control. Each terminal element of a machine system may have a high degree of intelligence and various control functions for the machine can be distributed and complex system transfer functions can be avoided. The calculation requirement of system computers can thus be drastically reduced.
2. Description of the Prior Art
Control is a technique of manipulating the input signals to a process plant so that the plant's output variables will yield a desired result. In general, there are two basic types of control systems: open loop control and closed loop control (a feedback system). In open loop control, the control is accomplished without any knowledge of the current state of the outputs. This system assumes that the plant operates without external disturbances which would cause the outputs of the plant to vary from those of a deterministic plant model established with pre-set coefficients. This type of control is inaccurate and is almost useless in practice because major disturbances exist in almost all processes.
In closed loop control systems, the actual values of the output parameters which are being controlled are fed back and compared with the desired output values to produce a "tracking error(s)" at any time during the plant control process. This type of control drives the tracking error(s) to zero when the plant's actual output agrees with the desired output state. In hierarchy, a closed loop control consists of four levels of evolution, depending on the sophistication and completeness of the control algorithms applied: (1) simple feedback control, (2) optimal control, (3) adaptive control, and (4) learning control.
Basically, a simple feedback control system comprises a controller which responds to the tracking error and manipulates the error according to a given plant transfer function. This action generates a plant control signal for regulating the outputs of a plant to a desired state. The control law used is simply an I/O (input/output) dynamic mapping so as to nullify the tracking error. The transfer function of the plant must be fully describable and constant.
An optimal control system is similar to a simple feedback control system, except the optimal controller manipulates the control process according to a "performance index" defined by the user. A performance index is a functional relationship which involves system state variables and control inputs such that the optimum operating conditions may be determined. It normally uses the minimum variance principles. There are numerous types of optimal controllers used in applications; typical examples include the following: (1) a minimum time control in which the final state is reached in the shortest possible period of time, (2) a minimum energy control so as to transfer the system from an initial state to a final state with a minimum expenditure of control energy, and (3) the wellknown PID (proportional-integral-derivative) controller which drives a system to match a desired dynamic characteristic by optimally setting the PID feedback gains
Unlike a simple feedback control system or an optimal control system, an adaptive control system is designed so as to modify its control law as the system operating conditions change so that the performance is always optimal. Therefore, for an adaptive system, the plant input/output state variables must be continuously available. In other words, an adaptive control system required an identification process so as to determine the I/O state variables in real time. Furthermore, the performance index must be continously calculated and the optimal control law changed to fit the new requirement. Consequently, an adaptive controller combines both system identification and control design in order to be self-tuning; however, it requires a complicated design and a fairly time-consuming control process. In fact, one of the major advantages of using adaptive control is the ability to overcome the tuning problem frequently encountered in feedback control applications which result from varying system operating conditions and external effects occuring during a control process.
A learning control System is designed so as to recognize familiar features and patterns of a situation and then, based on its past experience or learned behaviour, to react in an optimum manner. More specifically, a learning controller, if subjected to a new environment, learns how to react to that environment by adapting the control law. Nevertheless, if the system again experiences the environment it has previously learned, it will recognize the environment and change the control law as it did in the previous case rather than operate as an adaptive controller which requires that the system perform system identification, parameter estimation, and the proper control law for each control step regardless of past experiences. That is, a learning controller can avoid the time-consuming calculation of the adaptive control signal if the calculation is deemed unnecessary.
The Conference paper entitled "Terminal Intelligence For Computer Controlled Actuators" by I. T. Hong, T. Ito and E. C. Fitch at the Spring National Design Engineering Show and Conference at McCormick Place, Chicago, Ill. on Mar. 24,-27, 1986 discusses some of these problems.
It is to be noted that in a typical closed loop control, regardless of whether it is deterministic or adaptive, the controller requires a tracking error (obtained by comparing the desired output and the actual parameter values which are being controlled) for determining the plant control signal necessary so as to achieve a desired task. In theory, the plant's primary output parameters which are being controlled can be obtained under any condition. In practice, however, the primary output parameters are frequently not measurable or are very difficult to measure because of application restrictions, instrumentation problems, or safety-related problems. Hence, difficulties in obtaining the primary outputs can drastically handicap the practical application of closed loop control systems.
Accordingly, there is a need for a controller that can use the secondary output parameters which are measurable parameters other than the primary outputs from the plant so as to accomplish a desired control objective. Moreover, it is desirable that each controllable element in the system possess its own characteristic algorithm which may respond to the secondary outputs and to generate control signals accordingly. In such a case, system control is distributed to each element and thus avoids forming a complicated system transfer function that would increase the control's mathematical complexity and the calculation burden which are highly undesirable when control speed is important.